• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.241-245
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• 1995

This paper shows that the proposed fuzzy-sliding mode for SCARA robot control could reduce chattering problemed in sliding mode control and is robust against parameter uncertainties. It was very small quantities of chattering in the fuzzy-sliding mode control conpared with that in sliding mode control with two dead-band. In here, the sliding mode control with two dead-band is the method to reduce some chattering by changing into a continuous variable lower control input gain when a state value in pahase palne converged sithin two dead-band. But, the fuzzy-sliding mode control for more reducing chattering is the method to change control input by slicing mode into that by fuzzy rule within two dead-band. Simulations show that the effect of reducing chattering by the fuzzy-sliding mode is superior to sliding mode control with two dead-band.

• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.339-350
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• 2001

To overcome problems in tracking error related to the unmodeled dynamics in the high speed operation of industrial robots, many researchers have used sliding mode control, which is robust against parameter variations and payload changes. However, these algorithms cannot reduce the inherent chattering which is caused by excessive switching inputs around the sliding surface. This study proposes a fuzzy-sliding mode control algorithm to reduce the chattering of the sliding mode control by fuzzy rules within a pre-determined dead zone. Trajectory tracking simulations and experiments show that chattering can be reduced prominently by the fuzzy-sliding mode control algorithm compared to a sliding mode control with two dead zones, and the proposed control algorithm is robust to changes in payload. The proposed control algorithm is implemented to the SCARA (selected compliance articulated robot assembly) robot using a DSP (digital signal processor) for high speed calculations.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.221-224
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• 2000

We consider one of robust controller, fuzzy-sliding mode controller dealing with model uncertainty, simplified representation of nonlinear system, changed parameters of plant. We propose fuzzy-sliding mode algorithm which provides control input that has system states approaching the choosed sliding surface. This fuzzy controller has a rule base to get initial states converged on sliding surface. This algorithm Is applied to a transfer function of DC motor to be modeled simply and do position control of DC motor due to system parameters. We compare fuzzy-sliding mode controller to both sliding mode controller and fuzzy controller to identify roust control.

• Smart Structures and Systems
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• v.5 no.3
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• pp.261-277
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• 2009

A fuzzy-sliding mode controller is presented to control the dynamics of semi-active suspension systems of vehicles using magneto-rheological (MR) fluid dampers. A full car model is used to design and evaluate the performance of the proposed semi-active controlled suspension system. Four mixed mode MR dampers are designed, manufactured, and integrated with four independent sliding mode controllers. The siding mode controller is designed to decrease the energy consumption and maintain robustness. In order to overcome the chattering of the sliding mode controllers, a fuzzy logic control strategy is merged into the sliding mode controller. The proposed fuzzy-sliding mode controller is designed and fabricated. The performance of the semi-active suspensions is evaluated in both the time and frequency domains. The obtained results demonstrate that the proposed fuzzy-sliding mode controller can effectively suppress the vibration of vehicles and improve their ride comfort and handling stability. Furthermore, it is shown that the "chattering" of the sliding mode controller is smoothed when it is integrated with a fuzzy logic control strategy. Although the cost function of the fuzzy-sliding mode control is a slightly higher than that of a classical LQR controller, the control effectiveness and robustness are enhanced considerably.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.14-19
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• 1997

In this paper, a new fuzzy sliding mode control algorithm is presented for trajectory control of robot manipulators. A fuzzy logic is applied to a sliding mode control algorithm to have the sliding mode gain adjusted continuously through fuzzy logic rules. With this scheme, te stability and the robustness of the proposed fuzzy logic control algorithm are proved and ensured by the sliding mode control law. The fuzzy logic controller requires only a few tuning parameters to adjust. Computer simulation results are given to show that the proposed algorithm can handle uncertain systems with large parameter uncertainties and external disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.265-270
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• 1998

In this paper we introduce a modeling of wheeled mobile robot with a differential drive derived by R.M. DeSantis and using the dynamics model-ing with some disturbance term we control the wheeled mobile robot using fuzzy sliding mode control(FSMC) method. In a fuzzy control approach it is very difficult to prove the stability of the fuzzy controller. Therefore, to overcome that difficult proof of the stability in a fuzzy control method, we first propose a sliding mode controller and prove the stability of the proposed controller. Next, transforming the proposed sliding mode controller into a fuzzy sliding mode controller without changing the basic structure of the sliding mode con-troller, we easily obtain a fuzzy sliding mode con-troller(FSMC) whose stability is guaranteed with-out difficult stability proof procedure of the proposed FSMC.

• International Journal of Ocean System Engineering
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• v.3 no.2
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• pp.50-60
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• 2013

This paper presents a novel approach to the design of an adaptive fuzzy sliding mode controller for depth control of an autonomous underwater vehicle (AUV). So far, AUV's dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to estimate, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Hence, we propose an adaptive fuzzy sliding mode control with novel fuzzy adaptation technique for regulating vertical positioning in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximator are employed in such a way that slope of the linear sliding surface is updated by first fuzzy approximator, to shape tracking error dynamics in the sliding regime, while second fuzzy approximator change the supports of the output fuzzy membership function in the defuzzification inference module of fuzzy sliding mode control (FSMC) algorithm. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control FSMC technique.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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• pp.580-591
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• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1483-1486
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• 2002

In this paper, a new scheme of iterative loaming control of a robot manipulator is presented. The proposed method uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC), for the feedback. With this, the proposed method makes possible fDr fast iteration and has advantages that no linear approximation is used for the derivation of the learning law or in the stability proof Full proof of the convergence of the fuzzy sliding base learning scheme Is given.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.285-294
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• 2000

This paper shows that the proposed fuzzy-sliding mode control algorithm for a SCARA robot could reduce the chattering due to sliding mode control and is robust against a change of payload and parameter uncertainties. That is, the chattering can be reduced by changing control input for compensating disturbances into a control input by fuzzy rules within a pre-determined dead zone. The experimental results show that the chattering can be reduced more effectively by the fuzzy-sliding mode control algorithm than the sliding mode control with two dead zones. It is proved experimentally that the proposed control algorithm is robust to a change of payload. The proposed control algorithm is implemented to the SCARA robot using a DSP(board) for high speed calculations.